add a bunch of files and move .pkl file to models directory

app.py

@@ -8,7 +8,7 @@ from pycaret.classification import load_model, predict_model
 
 
 # load the trained model for predictions
-model = load_model('tuned_blend_specific_model_19112021')
+model = load_model('models/tuned_blend_specific_model_19112021')
 
 
 # define the function to call 

src/diarc.py

@@ -0,0 +1,136 @@
+# -*- coding: utf-8 -*-
+"""diarc.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Jyccp5Aeml-7oZABbACY2VTE9iQJg9Pe
+
+# Bismillahir Rahmaanir Raheem
+# Almadadh Ya Gause RadiAllahu Ta'alah Anh - Ameen
+
+# <font color=grey>DIabetes-related Amputation Risk Calculator (DIARC)</font>
+<b>_by Zakia Salod_</b>
+"""
+
+!pip install pycaret
+
+from pycaret.utils import version
+version()
+
+from pycaret.utils import enable_colab
+enable_colab()
+
+import numpy as np # Linear algebra
+import pandas as pd # Data processing, CSV file I/O (e.g. pd.read_csv)
+import matplotlib.pyplot as plt # For graphical representations of the data
+import seaborn as sns
+
+# Just to make sure the results are reproducible
+np.random.seed(1234)
+
+dataset = pd.read_excel('amputation_dataset.xlsx')
+
+print(dataset['AMPUTATION'].value_counts())
+
+ax = sns.countplot(x="AMPUTATION", data=dataset)
+
+# show the number of duplicate rows in this dataset
+dataset.duplicated(keep='first').sum()
+
+# remove the duplicate rows in this dataset
+# only keep the first instance of the row
+dataset = dataset.drop_duplicates(keep='first')
+
+print(dataset['AMPUTATION'].value_counts())
+
+ax = sns.countplot(x="AMPUTATION", data=dataset)
+
+dataset.head()
+
+# Under sample the dataset to handle the imbalance
+# Shuffle the Dataset.
+shuffled_dataset = dataset.sample(frac=1, random_state=4)
+
+# Put all the amputation class in a separate dataset.
+amputation_dataset = shuffled_dataset.loc[shuffled_dataset['AMPUTATION'] == 1]
+
+
+#Randomly select 105 observations from the non-amputation (majority class)
+non_amputation_dataset = shuffled_dataset.loc[shuffled_dataset['AMPUTATION'] == 0].sample(n=105,random_state=42)
+
+# Concatenate both dataframes again
+dataset = pd.concat([amputation_dataset, non_amputation_dataset])
+
+print(dataset['AMPUTATION'].value_counts())
+
+ax = sns.countplot(x="AMPUTATION", data=dataset)
+
+dataset.to_excel('amputation_removed_duplicates_and_balanced.xlsx')
+
+from pycaret.classification import *
+
+clf = setup(data = dataset, target = 'AMPUTATION',  session_id = 42)
+
+# display the dataset (X_train)
+get_config('X_train')
+# converts age from numeric to float
+# converts gender and diabetes_class (the two binary category variables) into label encoder conversion
+# so, gender_f ---> with value 1 indicating FEMALE is TRUE and value 0 indicating FEMALE is FALSE (and instead, MALE)
+# diabetes_class type 1 diabetes ---> value 1 indicates diabetes type 1 and value 0 means diabetes type 2
+# then, one hot encoding is applied to the race column (each race is split into separate columns, with value 1 denoting TRUE for that race)
+
+# display the dataset (y_train)
+get_config('y_train')
+
+best_model = compare_models(sort = 'AUC')
+
+# BLEND MODELS, ALHUM
+# create models for blending
+nb = create_model('nb')
+bagged_nb = ensemble_model(nb, method='Bagging')
+lr = create_model('lr')
+bagged_lr = ensemble_model(lr, method='Bagging')
+lda = create_model('lda')
+bagged_lda = ensemble_model(lda, method='Bagging')
+
+rf = create_model('rf')
+bagged_rf = ensemble_model(rf, method='Bagging')
+ada = create_model('ada')
+bagged_ada = ensemble_model(ada, method='Bagging')
+
+
+blend_specific = blend_models(estimator_list = [bagged_nb, bagged_lr, bagged_lda, bagged_rf, bagged_ada])
+
+# plot model
+plot_model(blend_specific)
+
+# tuning
+tuned_blend_specific = tune_model(blend_specific)
+
+evaluate_model(tuned_blend_specific)
+
+tuned_blend_specific_predictions = predict_model(tuned_blend_specific)
+
+# finalize model for deployment
+final_tuned_blend_specific = finalize_model(tuned_blend_specific)
+
+# save the model
+# creates a .pkl file
+save_model(tuned_blend_specific, "tuned_blend_specific_model_19112021", verbose=True)
+
+# display the dataset (X_test)
+get_config('X_test')
+
+# display the dataset (y_test)
+get_config('y_test')
+
+dataset2 = pd.read_excel('amputation_removed_duplicates_and_balanced.xlsx')
+
+!pip install pandas-profiling
+
+from pandas_profiling import ProfileReport
+
+profile = ProfileReport(dataset2, title="Pandas Profiling Report")
+
+profile.to_file("amputation_removed_duplicates_and_balanced_report.html")